Reputation-based Mechanism Research Articles

Group key management in the Internet of Things: Handling asynchronicity

Internet of Things (IoT) devices have permeated our immediate surroundings, becoming ubiquitous. These devices engage in continuous communication, with a significant aspect being group communications. Indeed, the latter are often more efficient than one-to-one interactions, rendering them well-suited for applications like e-health, federated learning, and military systems. However, security plays a pivotal role in these group communications, especially in safeguarding data confidentiality and user privacy. In this context, group key management protocols offer a mechanism for establishing shared group keys and updating them when needed. Nonetheless, the inherently asynchronous nature of IoT devices has proven to be a challenge. IoT devices are susceptible to unexpectedly entering an offline mode due to factors such as battery depletion, energy conservation, or loss of wireless connectivity. Consequently, the establishment of a group key for secure communications becomes a complex task. In this paper, we introduce an innovative approach to establishing group credentials asynchronously while ensuring robust security attributes, including Perfect Forward Secrecy (PFS) and Post-compromise Security (PCS). Our protocol is designed around blockchain technology, specifically smart contracts to embrace the distributed nature of IoT. Additionally, we incorporate a reputation-based mechanism to address the heterogeneity of IoT devices in terms of resource disparity. Evaluation results demonstrate the feasibility of our approach, along with reasonable performances.

Byzantine Agents’ Detection in Distributed Nash Equilibrium Seeking Algorithms Using an Adaptive Event-Triggered Scheme

In this article, a resilient, adaptive event-triggered distributed Nash equilibrium seeking algorithm is investigated for a class of multimicrogrid systems. Since the strategy of each microgrid affects others’ costs through total generation cost, such interaction among microgrids is modeled as a noncooperative game. To reduce unnecessary signal transmission among the aggregators, an adaptive algorithm, where threshold parameters are adjusted based on the behavior of the estimated signals in the last two iterations, is proposed. It is observed that when false data is injected into an agent, its estimated signals begin to diverge from the equilibrium point, and as a result, the threshold value becomes increasing. Furthermore, the misbehaving agents are identified and isolated using a reputation-based mechanism and are allowed to rejoin the network when they behave normally again. To deal with arbitrary behavior of users, receding horizon scheme of predictive control is used. Finally, a set of simulation results is presented to evaluate the effectiveness of the suggested algorithm.

Lightweight Reputation Management for Multi-Role Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), smart vehicles can fulfill multiple roles in either the information-centric IoV or the task-oriented IoV. However, malicious vehicles may undermine the trustiness of vehicles towards each other, and further damage these IoV networks. Given the multiple roles undertaken by vehicles in IoV networks, we aim to design a lightweight reputation-based mechanism for a hybrid IoV network in this article. This mechanism can realize real-time reputation updates and synchronization in the device-edge-cloud continuum. Simulation is conducted to validate the reputation management strategy in this article. We also discuss some opportunities and challenges to shed a light on the future research directions in this topic.

Evolutionary game model based on cumulative prospect theory for information management mechanism in SIoT

As nodes in Social Internet of Things (SIoT) become more intelligent, malicious information occurs more frequently and spreads more widely. This problem can severely affect the trustworthiness of services and applications in SIoT. Methods to effectively control malicious information spreading in SIoT are essential and necessary. Reputation mechanism provides a powerful tool to tackle this challenge. In this paper, we propose a reputation-based mechanism to activate the self-purification capacity of the SIoT network by balancing information conflicts triggered by reporters and supporters. In order to find the best rewarding and punishment strategy, a bilateral cumulative-prospect-based evolutionary game model of SIoT network information conflict is constructed. Using local stability analysis and numerical simulation, the evolutionary trends of the proposed game model under different theoretical application scenarios are analyzed. The findings indicate that the basic income and deposit of both sides, the popularity of information as well as the importance of the conformity effect all have a significant impact on the system's steady state and evolutionary path. The specific conditions that both participating sides of the game tend to treat conflicts relatively rationally are analyzed. Dynamic evolution analysis and sensitivity analysis of selected parameters show that basic income is positively related to smart object's feedback strategies, while deposit is negatively related to that. While weight of conformity effect or the information popularity goes up, the rising of feedback probability is observed. Based on the above results, suggestions on dynamic reward and punishment strategies are given. The proposed model is a helpful attempt to model the evolution of information spreading in SIoT networks, with the ability to simulate several well-known regularities of message dissemination. Proposed model and suggested quantitative strategies can be helpful to build feasible malicious information control facilities in SIoT networks.

Humans Reputational Concern: How the Reputation-Based Mechanisms of Social Evolution May Be Leveraged in the Modern Context of Social Media

In social lives, humans cooperate with or help others who are neither genetically related nor socially closed. This fact might help explain the underlying psychological mechanism of reputational concern that has been selected by evolution. However, the modern environment changes drastically compared to the ancestral past. How does the reputation-based mechanism evolved, and could it still secure its importance? This paper conducted a literature review and synthesized multiple evolutionary theories to examine the reputation-based mechanisms and consider how they may function in the modern context. The argument suggests that reputational concern embodies the evolutionary incentive to seek interactions that are favorable to fitness and reproduction (e.g., cooperation). Using the analysis, this paper then proposed an experimental test of social media exposure that shed light on how the mechanism may be leveraged to enhance cooperative behavior. Overall, by understanding the ultimate design of the psychology of reputation, we may utilize modern resources to harness its functions and promote positive change.

Emergency management through information crowdsourcing

This article proposes a new framework to model a scenario in which First Responders, citizens, smart devices, or robots explore the environment in an emergency situation, i.e., after an earthquake, assessing damages and searching for people needing assistance. While moving, the agents observe events and exchange the information collected with other agents encountered: to this end, they use messaging systems purposely adapted to use point-to-point network connections to allow local data exchange between agents even when global network connections are not available. As is common in Delay Tolerant Networks, exchanged messages are locally stored: when a global network is available, the agents can upload all the information collected by themselves and other agents they encountered to a Control Room or a database in the Cloud. Differently from traditional DTN algorithms such as Epidemic and Spray&Wait, we propose a solution that keeps track of agents that shared information along the path and assess the quality of the information collected by multiple agents through a reputation-based mechanism that is safer than majority voting. A simulator compatible with OpenStreetMap is presented, as well as simulated experiments in two Italian towns to validate the feasibility of the approach.

A Reputation-Based Mechanism for Transaction Processing in Blockchain Systems

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism.

A robust reputation iterative algorithm based on Z-statistics in a rating system with thorny objects

With the explosive growth of online alternative objects, the availability of a credible rating system could play a valuable role for users who are evaluating and/or choosing items. Due to the existence of spammers, many methods have been proposed that employ a reputation-based mechanism in the last few years. However, these methods only consider spammers’ behaviours but neglect the effect of thorny objects, which are difficult to evaluate because of their intrinsic uncertainty and complexity with respect to the problem context, knowledge gaps, and various user criteria. To solve this problem, we propose a new reputation iterative algorithm based on Z-statistics (ZS), which eliminates the effect of thorny objects and strengthens the ability to deal with spammers. In this article, the proposed method makes effectiveness comparisons with other typical methods using a rating example and a simulated rating system, and presents additional effectiveness and complexity analyses of these results. Finally, the experimental results also demonstrate that the ZS algorithm outperforms other methods in simultaneously dealing with the effects of spammers and thorny objects.

Computation offloading in D2D-enabled MCC for precedence-constrained components

The performance of mobile devices (i.e., storage, battery lifetime, and computation capacity) has been continuously enhanced thanks to performing computation offloading to either multiple remote clouds or the nearby mobile devices. In this paper, we design a framework of Device-to-Device (D2D) enabled mobile cloud computing, a new paradigm for computation offloading with a group of resource-rich devices, named D2D cloudlet, and several remote clouds. In order to incentivize mobile devices to participate in the D2D cloudlet, we design a credit and reputation-based mechanism. Accordingly, the proposed scheme holds the second-price reverse auction to price the resources of the D2D cloudlet. Moreover, the proposed scheme exploits users’ activities log in the D2D cloudlet to monitor how much the user shared its resources and the quality of services he provided and find users’ reputation value. Furthermore, the proposed scheme reduces the NP-hard nature of the decision algorithm and assigns each precedence-constrained component a proper site with polynomial time complexity. It is proved that our proposed scheme satisfies individual rationality, computational efficiency, truthfulness, and Pareto optimality. The extensive experiments demonstrate the effectiveness of the proposed scheme.

TFCrowd: a blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness

Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value-based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.

Mitigation of the spectrum sensing data falsifying attack in cognitive radio networks

ABSTRACT Cognitive radio networks (CRNs), which offer novel network architecture for utilising spectrums, have attracted significant attention in recent years. CRN users use spectrums opportunistically, which means they sense a channel, and if it is free, they start transmitting in that channel. In cooperative spectrum sensing, a secondary user (SU) decides about the presence of the primary user (PU) based on information from other SUs. Malicious SUs (MSUs) send false sensing information to other SUs so that they make wrong decisions about the spectrum status. As a result, an SU may transmit during the presence of the PU or may keep starving for the spectrum. In this paper, we propose a reputation-based mechanism which can minimise the effects of MSUs on decision making in cooperative spectrum sensing. Some of the SUs are selected as distributed fusion centres (DFCs), that are responsible for making decisions about the presence of PU and informing the reporting SUs. A DFC uses weighted majority voting among the reporting SUs, where weights are normalised reputation. The DFC updates reputations of SUs based on confidence of an election. If the majority wins by a significant margin, the confidence of the election is high. In this case, SUs that belong to the majority gain high reputations. We conduct extensive simulations to validate our proposed model.

Information-Aware Secure Routing in Wireless Sensor Networks.

Secure routing is crucial for wireless sensor networks (WSNs) because they are vulnerable to various attacks. In this paper, we propose a new secure routing protocol for WSNs in the presence of malicious nodes. For each relay node in the route, associated information such as its trust value and status is considered in the protocol. The trust value is defined as the attack probability of the node according to previous packet-forwarding behaviors, and the status is a hybrid metric that combines the residual energy and distance to the sink node. Therefore, the route generated by the protocol is secure against malicious attacks and globally optimal according to the associated information. We used an improved variant of the Dijkstra algorithm to generate the secure route for WSNs in the presence of malicious nodes. Compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model in the same simulation environment, the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm.

Interactional Moral Systems: A Model of Social Mechanisms for the Moral Regulation of Exchange Processes in Agent Societies

In this paper, we first introduce the concepts of moral agent and moral system of agent society and, in particular, the central concept of moral agent sensible to moral sanction. Next, we introduce the concepts of moral gain and moral loss in social exchanges and of reputation-based persistence of exchange processes. Following, we elaborate the notion of negotiated moral regulation of reputation-based persistent exchange processes. Finally, we combine those concepts in the notion of a reputation-based mechanism for the negotiation-driven moral regulation of exchange processes of agents that are sensible to moral sanctions.

PRIVANET: An Efficient Pseudonym Changing and Management Framework for Vehicular Ad-Hoc Networks

Protecting the location privacy is one of the main challenges in vehicular ad-hoc networks (VANETs). Although, standardization bodies, such as IEEE and ETSI, have adopted a pseudonym-based scheme as a solution for this problem, an efficient pseudonym changing and management is still an open issue. In this paper, we propose PRIVANET, a complete and efficient pseudonym changing and management framework. The PRIVANET has a hierarchical structure and considers the vehicular geographic area as a grid. Each cell of this grid contains one or many logical zones, called vehicular location privacy zones (VLPZs). These zones can easily be deployed over the widespread roadside infrastructures (RIs), such as gas stations, to provide a secure changing and management of pseudonyms. The proposed framework consists of different building blocks: 1) an effective VLPZ-based pseudonym changing strategy; 2) a reputation-based mechanism to motivate selfish vehicles to enter VLPZs; 3) an adapted user-centric privacy model; 4) a secure hybrid mechanism for the distribution of pseudonyms sets and CRLs; 5) a method to generate the IP and MAC addresses from the pseudonym; 6) a stochastic model to estimate the number of VLPZs required at a given cell; and 7) a mathematical model for an optimal placement of the VLPZs over RIs to reduce the transportation cost of vehicles in terms of time. An extensive simulation study using a realistic map and with real traffic mobility measurements is carried out to evaluate and validate the performance of the PRIVANET. The simulation results demonstrate the effectiveness of the proposed framework.

Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach

The core security of proof-of-work (PoW)-based blockchain networks, relies on PoW consensus algorithm and requires miners solving a crypto-puzzles of hash computation. However, the mining process in the blockchain is resource-intensive where only the first miner who completes full PoW will be rewarded. Therefore, miners exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this brief, we propose a new reputation-based mechanism for the PoW computation in the blockchain, in which miners are incentivized to conduct honest mining. Based on the game theory, we design a reputation-based algorithm to encourage honest mining of miners, and thereby increase the overall revenue of the pool. In addition, numerical illustrations are also presented to support the performance of our proposed mechanism.

Competing lending platforms, endogenous reputation, and fragility in microcredit markets

This paper shows that market fragility and mass default can arise in microcredit markets as a result of the strategic interaction between a microlender using a reputation-based mechanism and an informal lender using physical collateral. In our model, borrowers solve a dynamic programming problem which induces an endogenous equilibrium distribution of reputational capital. Because the quality of each lender’s pool of borrowers is affected by both lenders’ interest rates, lender reaction curves are non-monotonic and discontinuous. This can result in knife edge equilibria and mass default on the microlender precipitated by minor parametric perturbations. Fragility is exacerbated by borrower screening and sovereign risk, but ameliorated when microlenders have social welfare goals. Our results highlight the importance of studying the entire credit market rather than microfinance in isolation.

Endinginstitutions: Rule enforcement in self-governance systems

How are rules enforced in the absence of an organization with coercive powers? I examine the role of informal institutions in supporting self-enforcement of rules through ethnographic research on a popular form of community-based gambling in the Philippines. In ending, a reputation-based mechanism shapes exchange relations between bettors and bet-takers, and among members of a local community. Social norms about sharing one’s winnings ( balato) provide community members with an interest in the outcome of these exchange relations, thereby strengthening the ability of bettors to acquire information about the reputation of various bet-takers. In consequence, bet-takers exert efforts to safeguard their reputation, and comply with informal rules about the immediate and complete delivery of winnings. The findings suggest that when communities are small enough so that members are able to observe each other, but not too small so that no individual possesses all the relevant information, then social norms that generate effective transmission of reputational information provide sufficient institutional support for self-governance.

Coalition Game-Based Secure and Effective Clustering Communication in Vehicular Cyber-Physical System (VCPS).

In this paper, we address the low efficiency of cluster-based communication for the crossroad scenario in the Vehicular Cyber-Physical System (VCPS), which is due to the overload of the cluster head resulting from a large number of transmission bandwidth requirements. After formulating the issue as a coalition formation game, a coalition-based clustering strategy is proposed, which could converge into a Nash-stable partition to accomplish the clustering formation process. In the proposed strategy, the coalition utility is formulated by the relative velocity, relative position and the bandwidth availability ratio of vehicles among the cluster. Employing the coalition utility, the vehicles are denoted as the nodes that make the decision whether to switch to a new coalition or stay in the current coalition. Based on this, we can make full use of the bandwidth provided by cluster head under the requirement of clustering stability. Nevertheless, there exist selfish nodes during the clustering formation, so as to intend to benefit from networks. This behavior may degrade the communication quality and even destroy the cluster. Thus, we also present a reputation-based incentive and penalty mechanism to stop the selfish nodes from entering clusters. Numerical simulation results show that our strategy, CG-SECC, takes on a better performance for the tradeoff between the stability and efficiency of clustering communication. Besides, a case study demonstrates that the proposed incentive and penalty mechanism can play an important role in discovering and removing malicious nodes.

Enhanced node cooperation technique for outwitting selfish nodes in an ad hoc network

In an ad hoc network, selfish nodes do not cooperate in packet forwarding of other nodes to maximise their own welfare. Such nodes will have a negative effect on the performance of the network. In this study, an enhanced node cooperation technique (ENCT) is proposed which is a hybrid technique of reputation-based mechanism and incentive-based mechanism. It evaluates relaying nodes’ behaviour, calculates its reputation and outwitting selfish nodes by utilising receipts submitted by each relaying node in the path to a centralised authority rather than using promiscuous mode. A mathematical analysis is provided which demonstrates that the expected gain of reputation for a particular node is maximum if it adopts cooperative behaviour. To motivate nodes for cooperation, variable incentives are provided proportionate to their reputation and current behaviour. Simulation results demonstrate modification of reputation and incentive with varying number of selfish nodes, traffic connections, and topology with static and dynamic behaviour of selfish nodes. The result shows that the ENCT is robust, fair and efficient technique.

Reputation-based crowdsourced Wi-Fi topology discovery

The ubiquity of Wi-Fi networks, coupled with their anarchic deployment, gives rise to the problem of accurately determining their topology in order to employ it for tuning and optimization. Knowledge of the Wi-Fi topology is particularly important for reconfiguration mechanisms, such as channel assignment and power control schemes, which depend on it to operate. Furthermore, with the advent of WLAN-based positioning schemes, the robustness of the spatial databases where wireless topology is encoded becomes crucial for the accuracy of the respective localization algorithms. In this work, we propose a scheme where monitoring the topology of Wi-Fi deployments is crowdsourced to mobile users, who submit reports about Wi-Fi coverage in their vicinity. However, users cannot be assumed trustworthy. They can engage in fraudulent reporting, which, unless specific countermeasures are in place, can severely impact one’s view of the network topology. To this end, we have designed and implemented an architecture for accurate Wi-Fi topology discovery, devising a reputation-based mechanism to tackle realistic and simple to implement (and therefore potentially widespread) attacks. We show analytically and via simulation that, even in the presence of large numbers of attackers, our user-centric scheme significantly outperforms pure infrastructure-based approaches, where monitoring is carried out only by trusted Access Points.